Alexander Bocken
738875e89f
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Real-time shopping list with SSE sync between multiple clients, automatic item categorization using embedding-based classification + Bring icon matching, and card-based UI with category grouping. - SSE broadcast for live sync (add/check/remove items across tabs) - Hybrid categorizer: direct catalog lookup → category-scoped embedding search → per-category default icons, with DB caching - 388 Bring catalog icons matched via multilingual-e5-base embeddings - 170+ English→German icon aliases for reliable cross-language matching - Move cospend dashboard to /cospend/dash, /cospend redirects to list - Shopping icon on homepage links to /cospend/list
56 lines
1.7 KiB
TypeScript
56 lines
1.7 KiB
TypeScript
/**
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* Pre-compute sentence embeddings for shopping category representative items.
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* Uses multilingual-e5-base for good DE/EN understanding.
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*
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* Run: pnpm exec vite-node scripts/embed-shopping-categories.ts
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*/
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import { pipeline } from '@huggingface/transformers';
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import { writeFileSync } from 'fs';
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import { resolve } from 'path';
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const { CATEGORY_ITEMS } = await import('../src/lib/data/shoppingCategoryItems');
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const MODEL_NAME = 'Xenova/multilingual-e5-base';
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const OUTPUT_FILE = resolve('src/lib/data/shoppingCategoryEmbeddings.json');
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async function main() {
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console.log(`Loading model ${MODEL_NAME}...`);
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const embedder = await pipeline('feature-extraction', MODEL_NAME, {
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dtype: 'q8',
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});
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console.log(`Embedding ${CATEGORY_ITEMS.length} category items...`);
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const entries: { name: string; category: string; vector: number[] }[] = [];
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for (let i = 0; i < CATEGORY_ITEMS.length; i++) {
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const item = CATEGORY_ITEMS[i];
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// e5 models require "passage: " prefix for documents
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const result = await embedder(`passage: ${item.name}`, { pooling: 'mean', normalize: true });
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const vector = Array.from(result.data as Float32Array).map(v => Math.round(v * 10000) / 10000);
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entries.push({
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name: item.name,
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category: item.category,
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vector,
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});
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if ((i + 1) % 50 === 0) {
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console.log(` ${i + 1}/${CATEGORY_ITEMS.length}`);
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}
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}
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const output = {
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model: MODEL_NAME,
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dimensions: entries[0]?.vector.length || 768,
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count: entries.length,
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entries,
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};
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const json = JSON.stringify(output);
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writeFileSync(OUTPUT_FILE, json, 'utf-8');
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console.log(`Written ${OUTPUT_FILE} (${(json.length / 1024).toFixed(1)}KB, ${entries.length} entries)`);
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}
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main().catch(console.error);
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